What motivates coastal households to adapt pro-actively to sea-level rise and increasing flood risk? View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-08

AUTHORS

Jana Koerth, Athanasios T. Vafeidis, Jochen Hinkel, Horst Sterr

ABSTRACT

Individuals are able to contribute in reducing vulnerability to climate change and extreme events. This study addresses the question of what motivates coastal dwellers to adapt proactively to rising sea-levels and associated flood events. Data were collected through a questionnaire survey that was carried out in Germany and Denmark. Two elements based on variables of the Protection Motivation Theory were constructed, which depict the perceptions of flood risk and household-level adaptation. In addition, individual and residence-related characteristics were included. First, descriptive statistical analysis on adaptation behaviour and the two elements was conducted. Then, multiple regression analysis was applied to determine which variables influence the total number of implemented adaptation measures. Finally, the implementation of single adaptation measures was investigated using binary logistic regression analysis. Results indicate that personal experience is the main explanatory factor for adaptation behaviour while cognitive variables and personal history are also important. The independent variables that we used can, to a large extent, predict the implementation of measures that require small investments in terms of efforts and costs. However, the implementation of high-investment measures cannot be explained based on these variables. Our findings suggest that risk communication in coastal management should also integrate variables related to adaptation behaviour, and further research is needed to better understand the implementation of high-investment adaptation measures. More... »

PAGES

897-909

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10113-012-0399-x

DOI

http://dx.doi.org/10.1007/s10113-012-0399-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034548082


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